RESEARCH, ETHICS, AND SECURITY IN DATA SCIENCE - 2027/8

Module Overview

This module introduces students to the research skills required to engage in data science projects in both industry and academia, whilst also covering the relevant ethical and security considerations when designing and implementing data driven projects. Areas of specific concern for ethics and security in machine learning and statistical analysis are highlighted. Good practice in the responsible and effective use of generative AI for research development and academic writing will be demonstrated and critically discussed. This module serves as an important initial module towards planning and executing the dissertation project. It also provides a platform to facilitate team building skills and make the student aware of the challenges of working effectively with other people embracing equality, diversity and inclusion.

Module provider

Computer Science and Electronic Eng

Module Leader

GU Xiaowei (CS & EE)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Module Availability

Semester 2

Prerequisites / Co-requisites

N/A

Module content

Indicative module content is as follows: 

• Research skills, including literature searching and writing literature reviews. 
• Developing research proposals, and planning research projects. 
• Writing and structuring reports for a range of audiences. 
• Using generative AI in research and academic writing. 
• Ethical principles and academic integrity.  
• Bias in machine learning.  
• Working with personal data and legal frameworks for data privacy.  
• An overview of potential security threats and security fundamentals.  
• Data science specific security threats and security in the data lifecycle.

Assessment pattern

Alternative Assessment

An individual design project will be assigned if a student fails the group design project.

Assessment Strategy

The assessment strategy is designed to allow students to demonstrate their ability to work in a group to design a research project whilst taking into account ethical and security considerations. Thus, the summative assessment for this module consists of: 

• Group Research Project Design Assignment: This will involve working in a team to develop and submit a research project proposal with a executable plan. The performances of group members will be evaluated periodically to reflect the development of their teamworking skills and individuals' contributions.  The use of generative AI in the development of group report is encouraged and a disclaimer of AI use is required to be submitted together with the group report. (LO1-3) 
• Multiple Choice Examination: This will involve an examination of multiple choice questions to test ability relating to research, ethics and strategy. It's strategy will be to test the individuals discernment of key components in the process of forming new knowledge and transferring to innovative technical solutions with consideration of the wider societal and environmental impacts, while also giving consideration to associated professional practice. (LO2,4,5).

Formative assessment will be provided in this module in the following two ways:

• Students forming reflective evaluation of their performance in teamwork.
• Students forming reflective evaluation of their skills of using generative AI in the development of their group coursework solution.

Module aims

• The module aims to prepare students to undertake real-world data science projects by developing robust research capability alongside a comprehensive understanding of ethical responsibilities and security considerations in professional practice.
• In research skills, the module aims to equip students with the core competencies required to design and execute an independent project, including systematic literature search and critical evaluation, formulation of research questions and methodologies, structured project planning and management, and the informed and responsible use of generative AI tools.
• In ethics, the module aims to expose students to the relevant considerations for data science projects, including areas specific to data science such as algorithmic bias. The module aims to engage students in applying critical thinking to these questions, as well as having a solid understanding of existing regulations around personal data and privacy.
• For security, students should be aware of the common threats and also those specific to data science projects. The module aims to familiarise students with possible threats and also the fundamentals of security, as well as developing students ability to plan data science projects with end to end security in mind.

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to: 

Provide students with practical experience in conducting data science research, and applying critical thinking around ethical and security considerations for data science projects.  

The learning and teaching methods include:  

• Lectures to cover topics in the module.  
• Tutorials to help deepen the contextualisation of the material studied. 
• Captured content to support learning.
• Additional learning materials to enable self-paced learning. 
  
   

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: COMM072

Other information

The school of Computer Science and Electronic Engineering is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities in the following areas: Digital capabilities: Use of academic literature searching will be crucial in forming digital capability to find the relevant literature required and suitable books for reference. Furthermore referencing literature will be required. Employability: Ability to work within a diverse group of people to develop and plan a research project, will form a wide breadth of employability skills that are widely transferable. Global and cultural capabilities: Computer Science is a global language and the tools and languages used on this module can be used internationally. This module allows students to develop skills that will allow them to reason about and develop applications with global reach and collaborate with their peers around the world. Resourcefulness and Resilience: To develop resilience in completing a constant flow of work through a single semester in keeping to the task at hand regularly in order to meet the deadlines set. To ensure that this also is maintained where it involves dependency on working with others and to encourage one another in completing a task that will flourish with resourcefulness.

Programmes this module appears in

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2027/8 academic year.